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diff --git a/Histone_TFSEE/cluster_1_tfs.csv b/Histone_TFSEE/cluster_2_tfs.csv
similarity index 79%
rename from Histone_TFSEE/cluster_1_tfs.csv
rename to Histone_TFSEE/cluster_2_tfs.csv
index bfa739739841421e6a3776e711f22199737a30d3..1d530d8c40fce93605e4213b3284c3df72478b40 100644
--- a/Histone_TFSEE/cluster_1_tfs.csv
+++ b/Histone_TFSEE/cluster_2_tfs.csv
@@ -1,8 +1,6 @@
 symbol,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
 E2F6,12.765,12.05,13.85,15.7,13.92
-GATA4,0.08,55.85,60.32,168.535,57.74
 GLI2,17.815,4.495,1.7,0.37,1.075
-IRF1,2.89,2.39,0.305,3.11,2.56
 KLF1,0.32,0.805,1.18,0.59,0.45
 KLF12,3.72,4.185,8.88,8.085,9.125
 KLF16,17.695,18.915,10.375,6.245,6.515
@@ -11,7 +9,6 @@ MTF1,2.85,3.065,4.065,5.195,4.16
 NR2C2,11.36,11.285,12.59,10.985,11.54
 NRF1,12.775,12.645,15.66,15.51,16.51
 PLAG1,3.42,3.9,4.71,6.5,4.775
-RARA,6.195,10.955,11.025,33.47,26.135
 RREB1,10.315,17.075,10.515,12.59,14.675
 RXRA,13.38,31.955,56.265,27.41,34.875
 SMAD2,33.815,63.24,29.905,33.295,33.98
@@ -19,7 +16,5 @@ SMAD4,54.035,47.595,59.69,60.76,66.16
 SP2,6.75,6.92,10.8,8.075,12.81
 SP3,16.795,14.03,30.47,22.055,22.605
 SP8,1.075,2.045,0.515,0.295,0.05
-STAT2,14.685,26.38,24.935,48.685,37.59
 TCF3,136.215,124.305,134.515,121.675,163.045
 ZIC3,27.06,95.46,4.98,0.695,0.085
-ZNF740,7.14,14.92,27.79,28.23,20.005
diff --git a/Histone_TFSEE/cluster_3_enhancers.csv b/Histone_TFSEE/cluster_3_enhancers.csv
new file mode 100644
index 0000000000000000000000000000000000000000..6d2500ced6f52a7bd1ead24bd6240a83a2be4aac
--- /dev/null
+++ b/Histone_TFSEE/cluster_3_enhancers.csv
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diff --git a/Histone_TFSEE/cluster_3_tfs.csv b/Histone_TFSEE/cluster_3_tfs.csv
index a6cac7ff788d8462191204ce1e5329bf817e765a..ab3d3c1462f224ccc5c5e608317395e78a0dbc6d 100644
--- a/Histone_TFSEE/cluster_3_tfs.csv
+++ b/Histone_TFSEE/cluster_3_tfs.csv
@@ -1,18 +1,25 @@
 symbol,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10
+AHR,1.735,0.75,2.285,6.07,2.955
 EGR1,2.3,1.69,1.2,7.685,6.465
 EHF,0.0,0.0,0.035,16.115,6.33
 ELF1,6.8,11.92,9.41,29.9,29.37
+ELF3,2.21,3.685,3.88,52.51,13.54
 ELF4,0.89,0.73,2.49,0.68,3.03
 FOXP1,9.56,5.68,48.0,80.06,80.08
+GATA4,0.08,55.85,60.32,168.535,57.74
 GLIS3,0.075,0.315,0.16,1.19,10.685
 ID4,1.555,1.32,84.935,8.975,47.94
 INSM1,0.1,0.02,0.6,6.33,30.37
+IRF1,2.89,2.39,0.305,3.11,2.56
 KLF4,2.385,0.28,0.21,2.195,0.88
 KLF5,2.025,21.66,6.495,23.005,47.25
 NHLH1,0.155,0.16,0.235,0.675,0.67
 NR2F1,0.0,0.0,0.26,82.77,53.655
 PPARG,0.07,0.215,1.295,1.865,2.08
+RARA,6.195,10.955,11.025,33.47,26.135
 SMAD3,18.595,12.95,10.205,20.925,35.53
 SOX3,3.33,0.01,1.255,4.555,0.885
+STAT2,14.685,26.38,24.935,48.685,37.59
 TFAP2A,0.01,0.19,2.715,0.39,0.52
 ZFX,5.035,5.13,5.505,9.14,11.425
+ZNF740,7.14,14.92,27.79,28.23,20.005
diff --git a/Histone_TFSEE/clustering_tfs.csv b/Histone_TFSEE/clustering_tfs.csv
index 28585e8f0936cd4d9e59b764f62d387bde5f5a23..ce0e2488d0228d112bfce3598e096e26c25e7e56 100644
--- a/Histone_TFSEE/clustering_tfs.csv
+++ b/Histone_TFSEE/clustering_tfs.csv
@@ -1,169 +1,169 @@
 ,ES_D0,ES_D2,ES_D5,ES_D7,ES_D10,cluster
-AHR,0.119485788748,-0.617000119631,0.0919738733256,1.75810131217,0.132926965972,1
-ARNT,0.0664606139072,0.296568115173,-0.418291573839,-0.575948422012,-0.250500715249,1
-ASCL2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-BACH1,0.117256217236,-0.0885845039036,-0.00579228033837,-0.00988937752512,-0.611176690012,1
-CEBPA,-0.0768409437018,-0.617000119631,-0.1622005782,-0.062115163307,-0.371969903489,1
-CREB1,-0.0996116072585,-0.404077544825,-0.471264974621,-0.575948422012,-0.59354638216,1
-CREB3,-0.348473913473,-0.470648284666,-0.514857150651,-0.372963562635,-0.378242434521,1
-CREB3L1,-0.326840833995,-0.617000119631,-0.333165796186,-0.10972902678,-0.488063479862,1
-DDIT3,-0.403290568058,-0.165108054814,-0.514857150651,-0.360437158981,-0.532314121331,1
-DMBX1,-0.467935560061,-0.617000119631,-0.0805922255734,-0.575948422012,-0.611176690012,1
-E2F4,0.0939936591441,0.502049030447,-0.0506767946301,-0.575948422012,-0.380661824854,1
-E2F6,2.44465013541,1.91332081677,0.308918307377,-0.164581981956,-0.611176690012,0
-EBF1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
+AHR,0.119485788748,-0.617000119631,0.0919738733256,1.75810131217,0.132926965972,2
+ARNT,0.0664606139072,0.296568115173,-0.418291573839,-0.575948422012,-0.250500715249,0
+ASCL2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+BACH1,0.117256217236,-0.0885845039036,-0.00579228033837,-0.00988937752512,-0.611176690012,0
+CEBPA,-0.0768409437018,-0.617000119631,-0.1622005782,-0.062115163307,-0.371969903489,0
+CREB1,-0.0996116072585,-0.404077544825,-0.471264974621,-0.575948422012,-0.59354638216,0
+CREB3,-0.348473913473,-0.470648284666,-0.514857150651,-0.372963562635,-0.378242434521,0
+CREB3L1,-0.326840833995,-0.617000119631,-0.333165796186,-0.10972902678,-0.488063479862,0
+DDIT3,-0.403290568058,-0.165108054814,-0.514857150651,-0.360437158981,-0.532314121331,0
+DMBX1,-0.467935560061,-0.617000119631,-0.0805922255734,-0.575948422012,-0.611176690012,0
+E2F4,0.0939936591441,0.502049030447,-0.0506767946301,-0.575948422012,-0.380661824854,0
+E2F6,2.44465013541,1.91332081677,0.308918307377,-0.164581981956,-0.611176690012,1
+EBF1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
 EGR1,1.30496770826,0.480386773785,-0.514857150651,1.46883839186,0.736078066801,2
-EGR2,-0.467935560061,-0.617000119631,-0.514857150651,0.676963139439,-0.611176690012,1
-EGR3,-0.467935560061,-0.617000119631,-0.514857150651,0.367419996105,-0.0564313268642,1
-EGR4,-0.467935560061,-0.617000119631,-0.514857150651,1.51602291307,-0.611176690012,1
+EGR2,-0.467935560061,-0.617000119631,-0.514857150651,0.676963139439,-0.611176690012,0
+EGR3,-0.467935560061,-0.617000119631,-0.514857150651,0.367419996105,-0.0564313268642,0
+EGR4,-0.467935560061,-0.617000119631,-0.514857150651,1.51602291307,-0.611176690012,0
 EHF,-0.467935560061,-0.617000119631,-0.514857150651,1.60366494723,1.72229013884,2
 ELF1,-0.30713010762,-0.0287062962388,-0.514857150651,1.3475185769,1.87185887946,2
-ELF3,-0.435144845621,-0.420158495208,-0.514857150651,1.60366494723,0.557823067539,1
+ELF3,-0.435144845621,-0.420158495208,-0.514857150651,1.60366494723,0.557823067539,2
 ELF4,0.0322812650202,-0.0861032359366,1.27632596776,-0.575948422012,1.76570929846,2
-EOMES,-0.467935560061,-0.143343279804,-0.514857150651,-0.575948422012,-0.611176690012,1
-ESR1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-ESR2,-0.467935560061,-0.617000119631,-0.514857150651,-0.0170232532381,0.913617707926,1
-FIGLA,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
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+RREB1,0.370272666711,2.48898674304,-0.514857150651,-0.559853764068,-0.25302445872,1
+RUNX1,-0.467935560061,-0.617000119631,-0.514857150651,0.531297603426,0.446271742225,0
+RXRA,-0.392829507941,2.68921366029,2.46855456381,-0.575948422012,0.229643465551,1
+SCRT2,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+SIX3,-0.467935560061,-0.50730451569,-0.514857150651,-0.575948422012,-0.611176690012,0
+SMAD2,1.32695893444,4.09860897701,-0.211292912562,-0.575948422012,0.269395965445,1
 SMAD3,2.71809288446,2.56744551906,-0.514857150651,2.86069463567,5.73778084667,2
-SMAD4,2.99896587223,3.51936428454,1.47325164227,-0.575948422012,3.23755198783,0
-SNAI2,-0.467935560061,-0.617000119631,-0.251018361916,-0.575948422012,-0.349007214727,1
-SOX10,-0.446087504732,-0.479262050629,-0.493694242366,-0.575948422012,-0.611176690012,1
-SOX11,-0.370171639992,-0.617000119631,-0.385293248236,-0.405712613931,-0.57198896913,1
+SMAD4,2.99896587223,3.51936428454,1.47325164227,-0.575948422012,3.23755198783,1
+SNAI2,-0.467935560061,-0.617000119631,-0.251018361916,-0.575948422012,-0.349007214727,0
+SOX10,-0.446087504732,-0.479262050629,-0.493694242366,-0.575948422012,-0.611176690012,0
+SOX11,-0.370171639992,-0.617000119631,-0.385293248236,-0.405712613931,-0.57198896913,0
 SOX3,0.998032501965,-0.617000119631,1.04602314069,1.36594949085,0.516632464301,2
-SOX4,-0.448145523179,-0.617000119631,-0.445554590886,-0.412763782683,-0.395562811861,1
-SOX8,-0.394824929083,-0.527673020618,-0.514857150651,-0.506293308256,-0.535283615859,1
-SP1,0.194667332067,1.01265320395,0.0975460934684,-0.575948422012,-0.227394370345,1
-SP2,1.60946133653,1.31715291911,1.34954210612,-0.575948422012,0.933314845068,0
-SP3,3.15936737741,1.28070574007,4.74727624735,-0.575948422012,0.396151641168,0
-SP4,0.309579630698,-0.289297885725,0.109844955244,-0.575948422012,-0.415049173059,1
-SP8,2.14112020408,3.23468501076,2.90778061566,-0.575948422012,-0.611176690012,0
-SPIB,0.452193476686,-0.617000119631,-0.514857150651,-0.575948422012,0.148822784228,1
-SPIC,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-SPZ1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-SREBF1,-0.434305044269,-0.495448340115,-0.486076303709,-0.575948422012,-0.566525707842,1
-SREBF2,-0.313904968112,-0.444467736306,-0.419677976342,-0.575948422012,-0.606492252354,1
-STAT1,-0.0936328867055,0.95182938432,-0.364760710895,-0.575948422012,0.809189029537,1
-STAT2,-0.362753978743,2.25109859577,-0.514857150651,0.830064058809,0.345090292951,0
-TBR1,-0.467935560061,-0.143343279804,-0.514857150651,-0.575948422012,-0.611176690012,1
-TBX1,-0.467935560061,-0.617000119631,0.354159415725,0.020421111561,0.0456626598225,1
-TBX15,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-TBX20,-0.467935560061,-0.143343279804,0.347856513593,0.0423655764682,0.287101749709,1
-TBX21,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-TBX4,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-TBX5,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-TCF12,0.12218297087,0.00439002647852,-0.393600966324,-0.575948422012,1.4047986152,1
-TCF21,-0.467935560061,-0.617000119631,-0.514857150651,0.971092147887,0.346837407652,1
-TCF3,2.38668173699,1.29122057938,0.660514253741,-0.575948422012,2.10463922242,0
-TCF4,0.462474632711,-0.117254799103,0.0612994884503,1.00765107434,-0.611176690012,1
-TEAD3,-0.395906084319,-0.435014829183,-0.405329162638,-0.575948422012,-0.473296903543,1
+SOX4,-0.448145523179,-0.617000119631,-0.445554590886,-0.412763782683,-0.395562811861,0
+SOX8,-0.394824929083,-0.527673020618,-0.514857150651,-0.506293308256,-0.535283615859,0
+SP1,0.194667332067,1.01265320395,0.0975460934684,-0.575948422012,-0.227394370345,0
+SP2,1.60946133653,1.31715291911,1.34954210612,-0.575948422012,0.933314845068,1
+SP3,3.15936737741,1.28070574007,4.74727624735,-0.575948422012,0.396151641168,1
+SP4,0.309579630698,-0.289297885725,0.109844955244,-0.575948422012,-0.415049173059,0
+SP8,2.14112020408,3.23468501076,2.90778061566,-0.575948422012,-0.611176690012,1
+SPIB,0.452193476686,-0.617000119631,-0.514857150651,-0.575948422012,0.148822784228,0
+SPIC,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+SPZ1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+SREBF1,-0.434305044269,-0.495448340115,-0.486076303709,-0.575948422012,-0.566525707842,0
+SREBF2,-0.313904968112,-0.444467736306,-0.419677976342,-0.575948422012,-0.606492252354,0
+STAT1,-0.0936328867055,0.95182938432,-0.364760710895,-0.575948422012,0.809189029537,0
+STAT2,-0.362753978743,2.25109859577,-0.514857150651,0.830064058809,0.345090292951,2
+TBR1,-0.467935560061,-0.143343279804,-0.514857150651,-0.575948422012,-0.611176690012,0
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+TBX15,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+TBX20,-0.467935560061,-0.143343279804,0.347856513593,0.0423655764682,0.287101749709,0
+TBX21,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+TBX4,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+TBX5,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+TCF12,0.12218297087,0.00439002647852,-0.393600966324,-0.575948422012,1.4047986152,0
+TCF21,-0.467935560061,-0.617000119631,-0.514857150651,0.971092147887,0.346837407652,0
+TCF3,2.38668173699,1.29122057938,0.660514253741,-0.575948422012,2.10463922242,1
+TCF4,0.462474632711,-0.117254799103,0.0612994884503,1.00765107434,-0.611176690012,0
+TEAD3,-0.395906084319,-0.435014829183,-0.405329162638,-0.575948422012,-0.473296903543,0
 TFAP2A,-0.467935560061,-0.617000119631,2.82987667746,-0.575948422012,1.74653580338,2
-TFAP4,0.359885723656,0.201470354284,1.32669236979,-0.575948422012,-0.49000861373,1
-TGIF1,-0.32118751625,-0.143343279804,-0.451178494877,-0.225236933826,-0.611176690012,1
-TGIF2,-0.229460290867,-0.530763950097,0.354159415725,-0.575948422012,0.313772118802,1
-TLX1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-USF1,-0.359451937781,-0.398283757642,-0.514857150651,-0.431985414245,-0.56155526598,1
-VDR,-0.427392714796,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-ZBTB18,-0.390840273111,-0.143343279804,-0.514857150651,-0.272681265235,-0.0418695095094,1
-ZBTB7A,-0.443689192936,-0.579749762625,-0.489891825855,-0.575948422012,-0.609316475405,1
-ZBTB7C,-0.467935560061,-0.243921564486,-0.514857150651,-0.0521894122516,-0.361500274226,1
-ZEB1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
+TFAP4,0.359885723656,0.201470354284,1.32669236979,-0.575948422012,-0.49000861373,0
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+ZEB1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
 ZFX,0.913997279421,0.891318105346,-0.514857150651,1.25770207688,2.84549185984,2
-ZIC1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-ZIC3,1.01470297303,1.15059107303,1.84461260591,0.708252611734,-0.611176690012,0
-ZIC4,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,1
-ZNF143,-0.32651763902,-0.28074478482,-0.439144099096,-0.575948422012,-0.519307010435,1
-ZNF263,0.717845226842,0.628496902309,0.332994458407,-0.575948422012,0.393931559393,1
-ZNF354C,-0.241629336797,-0.163808712186,-0.0644354751023,-0.38925901247,-0.611176690012,1
-ZNF740,-0.467935560061,3.10584518315,5.83677913492,4.31546393347,1.62877555735,0
+ZIC1,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+ZIC3,1.01470297303,1.15059107303,1.84461260591,0.708252611734,-0.611176690012,1
+ZIC4,-0.467935560061,-0.617000119631,-0.514857150651,-0.575948422012,-0.611176690012,0
+ZNF143,-0.32651763902,-0.28074478482,-0.439144099096,-0.575948422012,-0.519307010435,0
+ZNF263,0.717845226842,0.628496902309,0.332994458407,-0.575948422012,0.393931559393,0
+ZNF354C,-0.241629336797,-0.163808712186,-0.0644354751023,-0.38925901247,-0.611176690012,0
+ZNF740,-0.467935560061,3.10584518315,5.83677913492,4.31546393347,1.62877555735,2
diff --git a/Histone_TFSEE/k_means_clustering.png b/Histone_TFSEE/k_means_clustering.png
index 56eea6f9d5bf8a2f30119e5f809d203057e4a311..c5ce38717eb9792b21a5b7ea92f5e342663e9238 100644
Binary files a/Histone_TFSEE/k_means_clustering.png and b/Histone_TFSEE/k_means_clustering.png differ
diff --git a/Histone_TFSEE/matrix_analysis.py b/Histone_TFSEE/matrix_analysis.py
index 35b27d3ea99dcb06c7e7e679bcca787d98306c2f..843454daa3d591d53af282a7c10b78aa1c8df02a 100644
--- a/Histone_TFSEE/matrix_analysis.py
+++ b/Histone_TFSEE/matrix_analysis.py
@@ -364,10 +364,10 @@ plt.style.use('classic')
 
 
 #TNSE
-tnse = TSNE(n_components=2,verbose=2,learning_rate=100,perplexity=50)
-tnse_fit = tnse.fit_transform(cell_tf_values)
+tnse = TSNE(n_components=2,verbose=2,learning_rate=200,perplexity=50)
+tnse_fit = tnse.fit_transform(cell_tf_values_std)
 
-k_model = KMeans(n_clusters=3).fit(cell_tf_values)
+k_model = KMeans(n_clusters=3,random_state=1).fit(cell_tf_values_std)
 labels = k_model.labels_
 centroids = k_model.cluster_centers_
 
@@ -375,7 +375,7 @@ centroids = k_model.cluster_centers_
 vis_x = tnse_fit[:, 0]
 vis_y = tnse_fit[:, 1]
 
-plt.scatter(vis_x, vis_y, c=labels, cmap=plt.cm.get_cmap("jet", 3),facecolor='white',marker='o',s=100,linewidth=2)
+plt.scatter(vis_x, vis_y, c=labels, cmap=plt.cm.get_cmap("jet", 3),facecolor='white',marker='o',s=120,linewidth=2)
 #plt.colorbar(ticks=None)
 plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
@@ -385,7 +385,7 @@ plt.clf()
 
 
 
-k_model = KMeans(n_clusters=3,random_state=1).fit(cell_tf_values_std)
+k_model = KMeans(n_clusters=3,random_state=0).fit(cell_tf_values_std)
 labels = k_model.labels_
 centroids = k_model.cluster_centers_
 
@@ -415,7 +415,7 @@ cell_tf_values_std_cluster_3 = cell_tf_values_std_cluster.loc[cell_tf_values_std
 
 # col_colors
 colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
-medianprops = dict(linestyle='-', linewidth=4, color='black')
+medianprops = dict(linestyle='-', linewidth=2, color='black')
 
 box = cell_tf_values_std_cluster_1.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
 plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
@@ -428,7 +428,7 @@ plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
 plt.tick_params(top='off', right='off')
 plt.grid(b=False)
-plt.ylim((-1.5,5.5))
+plt.ylim((-1,1))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_1.png')
 plt.clf()
@@ -445,7 +445,7 @@ plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
 plt.tick_params(top='off', right='off')
 plt.grid(b=False)
-plt.ylim((-1,1))
+plt.ylim((-1.5,5.5))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_2.png')
 plt.clf()
@@ -519,13 +519,13 @@ scipy.stats.ranksums(cell_tf_values_std_cluster_3['ES_D5'],cell_tf_values_std_cl
 scipy.stats.ranksums(cell_tf_values_std_cluster_3['ES_D7'],cell_tf_values_std_cluster_3['ES_D10'])
 
 
-# Look at Cluster 4 for expression of TF's
-cluster4_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_4.index.values]
-cluster4_tfs.to_csv("cluster_4_tfs.csv")
+# Look at Cluster 2 for expression of TF's
+cluster2_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_2.index.values]
+cluster2_tfs.to_csv("cluster_2_tfs.csv")
 
-box = cluster4_tfs.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
-plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+box = cluster2_tfs.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
 
 for patch, color in zip(box['boxes'], colors):
     patch.set_facecolor(color)
@@ -534,37 +534,40 @@ plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
 plt.tick_params(top='off', right='off')
 plt.grid(b=False)
-plt.ylim((-5,65))
+plt.ylim((-5,55))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
-plt.savefig('box_plot_cluster_4_tfs_fpkm.png')
+plt.savefig('box_plot_cluster_2_tfs_fpkm.png')
 plt.clf()
 
 # Cluster tfs 1 e-3
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D2'])
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D5'])
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D7'])
-scipy.stats.ranksums(cluster4_tfs['ES_D0'],cluster4_tfs['ES_D10'])
-
-
-scipy.stats.ranksums(cluster4_tfs['ES_D2'],cluster4_tfs['ES_D5'])
-scipy.stats.ranksums(cluster4_tfs['ES_D2'],cluster4_tfs['ES_D7'])
-scipy.stats.ranksums(cluster4_tfs['ES_D2'],cluster4_tfs['ES_D10'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D2'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D5'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D7'])
+scipy.stats.ranksums(cluster2_tfs['ES_D0'],cluster2_tfs['ES_D10'])
 
-scipy.stats.ranksums(cluster4_tfs['ES_D5'],cluster4_tfs['ES_D7'])
-scipy.stats.ranksums(cluster4_tfs['ES_D5'],cluster4_tfs['ES_D10'])
 
-scipy.stats.ranksums(cluster4_tfs['ES_D7'],cluster4_tfs['ES_D10'])
+scipy.stats.ranksums(cluster2_tfs['ES_D2'],cluster2_tfs['ES_D5'])
+scipy.stats.ranksums(cluster2_tfs['ES_D2'],cluster2_tfs['ES_D7'])
+scipy.stats.ranksums(cluster2_tfs['ES_D2'],cluster2_tfs['ES_D10'])
 
+scipy.stats.ranksums(cluster2_tfs['ES_D5'],cluster2_tfs['ES_D7'])
+scipy.stats.ranksums(cluster2_tfs['ES_D5'],cluster2_tfs['ES_D10'])
 
+scipy.stats.ranksums(cluster2_tfs['ES_D7'],cluster2_tfs['ES_D10'])
 
+cluster2_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_2.index.values]
+cluster2_enhancers = only_H3K27ac_values.loc[cluster2_motifs.loc[:, (cluster2_motifs >1).any(axis=0)].columns.values]
+cluster_2_DO = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D0'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D0'] >= 1)].index.values))
+cluster_2_D2 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D2'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D2'] >= 1)].index.values))
+cluster_2_D5 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D5'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D5'] >= 1)].index.values))
+cluster_2_expressed_enhancers = list(set(cluster_2_DO) | set(cluster_2_D2) | set(cluster_2_D5))
+needed_rows = [row for row in cluster2_enhancers.index if row in cluster_2_expressed_enhancers]
+cluster2_enhancers_expressed = cluster2_enhancers.loc[needed_rows]
+cluster2_enhancers_expressed.to_csv("cluster_2_enhancers.csv")
 
-cluster4_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_4.index.values]
-cluster4_enhancers = only_rpkm_values.loc[cluster4_motifs.loc[:, (cluster4_motifs != 0).all(axis=0)].columns.values]
-cluster4_enhancers.to_csv("cluster_4_enhancers.csv")
-
-box = cluster4_enhancers.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
-plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+box = cluster2_enhancers_expressed.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
 
 for patch, color in zip(box['boxes'], colors):
     patch.set_facecolor(color)
@@ -573,26 +576,27 @@ plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
 plt.tick_params(top='off', right='off')
 plt.grid(b=False)
-plt.ylim((-5,105))
+plt.ylim((-1,6))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
-plt.savefig('box_plot_cluster_4_enhancers_rpkm.png')
+plt.savefig('box_plot_cluster_2_enhancers_rpkm.png')
 plt.clf()
 
 # Cluster tfs 1 e-4
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D2'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D0'],cluster4_enhancers['ES_D10'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D0'],cluster2_enhancers_expressed['ES_D10'])
 
 
-scipy.stats.ranksums(cluster4_enhancers['ES_D2'],cluster4_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D2'],cluster4_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D2'],cluster4_enhancers['ES_D10'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D2'],cluster2_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D2'],cluster2_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D2'],cluster2_enhancers_expressed['ES_D10'])
 
-scipy.stats.ranksums(cluster4_enhancers['ES_D5'],cluster4_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster4_enhancers['ES_D5'],cluster4_enhancers['ES_D10'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D5'],cluster2_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D5'],cluster2_enhancers_expressed['ES_D10'])
+
+scipy.stats.ranksums(cluster2_enhancers_expressed['ES_D7'],cluster2_enhancers_expressed['ES_D10'])
 
-scipy.stats.ranksums(cluster4_enhancers['ES_D7'],cluster4_enhancers['ES_D10'])
 
 
 # Look at Cluster 3 for expression of TF's
@@ -600,8 +604,8 @@ cluster3_tfs = tf_cell_lines.loc[cell_tf_values_std_cluster_3.index.values]
 cluster3_tfs.to_csv("cluster_3_tfs.csv")
 
 box = cluster3_tfs.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
-plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
 
 for patch, color in zip(box['boxes'], colors):
     patch.set_facecolor(color)
@@ -610,12 +614,12 @@ plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
 plt.tick_params(top='off', right='off')
 plt.grid(b=False)
-plt.ylim((-5,55))
+plt.ylim((-5,90))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_3_tfs_fpkm.png')
 plt.clf()
 
-# Cluster tfs 1 e-3 (NS)
+# Cluster tfs 1 e-2
 scipy.stats.ranksums(cluster3_tfs['ES_D0'],cluster3_tfs['ES_D2'])
 scipy.stats.ranksums(cluster3_tfs['ES_D0'],cluster3_tfs['ES_D5'])
 scipy.stats.ranksums(cluster3_tfs['ES_D0'],cluster3_tfs['ES_D7'])
@@ -635,12 +639,18 @@ scipy.stats.ranksums(cluster3_tfs['ES_D7'],cluster3_tfs['ES_D10'])
 
 
 cluster3_motifs = motif_enhancers.loc[cell_tf_values_std_cluster_3.index.values]
-cluster3_enhancers = only_rpkm_values.loc[cluster3_motifs.loc[:, (cluster3_motifs != 0).all(axis=0)].columns.values]
-cluster3_enhancers.to_csv("cluster_3_enhancers.csv")
+cluster3_enhancers = only_H3K27ac_values.loc[cluster3_motifs.loc[:, (cluster3_motifs >1).any(axis=0)].columns.values]
+cluster_3_D7 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D7'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D7'] >= 1)].index.values))
+cluster_3_D10 = list(set(H3K27ac_robust_filtered[(H3K27ac_robust_filtered['ES_D10'] >= 1)].index.values) & set(H3K4me1_robust_filtered[(H3K4me1_robust_filtered['ES_D10'] >= 1)].index.values))
+cluster_3_expressed_enhancers = list(set(cluster_3_D7) | set(cluster_3_D10) )
+needed_rows = [row for row in cluster3_enhancers.index if row in cluster_3_expressed_enhancers]
+cluster3_enhancers_expressed = cluster3_enhancers.loc[needed_rows]
+cluster3_enhancers_expressed.to_csv("cluster_3_enhancers.csv")
 
-box = cluster3_enhancers.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
-plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 3)
-plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 3)
+
+box = cluster3_enhancers_expressed.boxplot(column=['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'],patch_artist=True,showfliers=False,manage_xticks=False,widths = 0.6, medianprops = medianprops)
+plt.setp(box['whiskers'], color='k', linestyle='-', linewidth = 5)
+plt.setp(box['boxes'], color='k', linestyle='-', linewidth = 5)
 
 for patch, color in zip(box['boxes'], colors):
     patch.set_facecolor(color)
@@ -649,140 +659,23 @@ plt.tick_params(axis='y', direction='out')
 plt.tick_params(axis='x', direction='out')
 plt.tick_params(top='off', right='off')
 plt.grid(b=False)
-plt.ylim((-5,65))
+plt.ylim((-1,6))
 plt.xticks([1,2,3,4,5], ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10'])
 plt.savefig('box_plot_cluster_3_enhancers_rpkm.png')
 plt.clf()
 
-# Cluster tfs 1 e-12
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D2'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D0'],cluster3_enhancers['ES_D10'])
-
-
-scipy.stats.ranksums(cluster3_enhancers['ES_D2'],cluster3_enhancers['ES_D5'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D2'],cluster3_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D2'],cluster3_enhancers['ES_D10'])
-
-scipy.stats.ranksums(cluster3_enhancers['ES_D5'],cluster3_enhancers['ES_D7'])
-scipy.stats.ranksums(cluster3_enhancers['ES_D5'],cluster3_enhancers['ES_D10'])
-
-scipy.stats.ranksums(cluster3_enhancers['ES_D7'],cluster3_enhancers['ES_D10'])
-
-
-## Analysis of only RNA-seq
-# 1. Z-score Standardize for each cell line to see important TF's
-scaler = preprocessing.StandardScaler()
-norm = scaler.fit_transform(tf_scaled_ordered.values)
-tf_scaled_std = pd.DataFrame(data=norm, columns=list(tf_scaled_ordered.columns.values), index = tf_scaled_ordered.index )
-
-# Seaborn settings
-sns.axes_style({'image.cmap': u'Blacks','lines.linewidth': 100.0})
-
-# Cluster Heatmap
-sns.set_context("paper")
-hmap = sns.clustermap(tf_scaled_std,xticklabels=True, yticklabels=True, cmap="RdBu_r", method = "complete", metric = "euclidean", figsize=(20, 20), col_colors=sns.color_palette(cell_tf_values_colors))
-plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
-plt.savefig('final_full_cluster_heatmap_rna-seq.png')
-
-labels = [item.get_text() for item in hmap.ax_heatmap.yaxis.get_majorticklabels()]
-labels.reverse()
-with  open("final_full_cluster_heatmap_rna-seq.csv", 'wb') as csv_file:
-    wr = csv.writer(csv_file,dialect='excel',quoting=csv.QUOTE_ALL)
-    for tf in labels:
-        wr.writerow([tf,])
-
-# 2. Reorder based on clustering
-reorder_clustering = tf_scaled_std.columns.values[hmap.dendrogram_col.reordered_ind]
-tf_scaled_std_ordered = tf_scaled_std[reorder_clustering]
-reindex_cluserting = tf_scaled_std.index.values[hmap.dendrogram_row.reordered_ind]
-tf_scaled_std_ordered = tf_scaled_std_ordered.reindex(reindex_cluserting)
-tf_scaled_std_ordered.to_csv("final_full_cluster_z_score-rnaseq.csv", encoding='utf-8')
-
-
-## Analysis of only GRO-seq data
-# 1. Make Score Matrix
-## Enhancers RPKM x Motif Enhancers
-motif_cell_line = motif_enhancers_scaled.dot(rpkm_robust_filtered)
-needed_rows = [row for row in motif_cell_line.index if row in list(tf_scaled.index)]
-motif_cell_line_filtered_tfs = motif_cell_line.loc[needed_rows]
-motif_cell_line_filtered_tfs.columns =  ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10']
-motif_cell_line_filtered_tfs = motif_cell_line_filtered_tfs[reorder]
-
-# 2. Z-score Standardize for each cell line to see important TF's
-scaler = preprocessing.StandardScaler()
-norm = scaler.fit_transform(motif_cell_line_filtered_tfs.values)
-motif_cell_line_filtered_tfs_std = pd.DataFrame(data=norm, columns=list(motif_cell_line_filtered_tfs.columns.values), index = motif_cell_line_filtered_tfs.index )
-
-# Seaborn settings
-sns.axes_style({'image.cmap': u'Blacks','lines.linewidth': 100.0})
-
-# Cluster Heatmap
-sns.set_context("paper")
-hmap = sns.clustermap(motif_cell_line_filtered_tfs_std,xticklabels=True, yticklabels=True, cmap="RdBu_r", method = "complete", metric = "euclidean", figsize=(20, 20), col_colors=sns.color_palette(cell_tf_values_colors))
-plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
-plt.savefig('final_full_cluster_heatmap_gro-seq.png')
-
-labels = [item.get_text() for item in hmap.ax_heatmap.yaxis.get_majorticklabels()]
-labels.reverse()
-with  open("final_full_cluster_heatmap_gro-seq.csv", 'wb') as csv_file:
-    wr = csv.writer(csv_file,dialect='excel',quoting=csv.QUOTE_ALL)
-    for tf in labels:
-        wr.writerow([tf,])
-
-# 3. Reorder based on clustering
-reorder_clustering = motif_cell_line_filtered_tfs_std.columns.values[hmap.dendrogram_col.reordered_ind]
-motif_cell_line_filtered_tfs_std_ordered = motif_cell_line_filtered_tfs_std[reorder_clustering]
-reindex_cluserting = motif_cell_line_filtered_tfs_std.index.values[hmap.dendrogram_row.reordered_ind]
-motif_cell_line_filtered_tfs_std_ordered = motif_cell_line_filtered_tfs_std_ordered.reindex(reindex_cluserting)
-motif_cell_line_filtered_tfs_std_ordered.to_csv("final_full_cluster_z_score-groseq.csv", encoding='utf-8')
-
-## Analysis of only GRO-seq data + RNA-seq
-
-# 1. Make Score Matrix
-## Enhancers RPKM x Motif Enhancers
-motif_cell_line = motif_enhancers_scaled.dot(rpkm_robust_filtered)
-needed_rows = [row for row in motif_cell_line.index if row in list(tf_scaled.index)]
-motif_cell_line_filtered_tfs = motif_cell_line.loc[needed_rows]
-motif_cell_line_filtered_tfs.columns = ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10']
-motif_cell_line_filtered_tfs = motif_cell_line_filtered_tfs[reorder]
-
-# reindex
-tf_scaled_ordered = tf_scaled.reindex(list(motif_cell_line_filtered_tfs.index))
-tf_scaled_ordered = tf_scaled_ordered[reorder]
-
-# 2. .multiply() to to Element-by-element multiplication Score Enhancers by TF
-cell_tf_values = motif_cell_line_filtered_tfs.multiply(tf_scaled_ordered)
-cell_tf_values.columns = ['ES_D0', 'ES_D2', 'ES_D5', 'ES_D7',  'ES_D10']
-cell_tf_values_colors = ["#FFD66F","#2E6A44","#862743", "#4FA6C7", "#3398CC"]
-
-# 3. Z-score Standardize for each cell line to see important TF's
-scaler = preprocessing.StandardScaler()
-norm = scaler.fit_transform(cell_tf_values.values)
-cell_tf_values_std = pd.DataFrame(data=norm, columns=list(cell_tf_values.columns.values), index = cell_tf_values.index )
-
-
-# Seaborn settings
-sns.axes_style({'image.cmap': u'Blacks','lines.linewidth': 100.0})
+# Cluster tfs 1 e-4
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D2'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D0'],cluster3_enhancers_expressed['ES_D10'])
 
-# Cluster Heatmap
-sns.set_context("paper")
-hmap = sns.clustermap(cell_tf_values_std,xticklabels=True, yticklabels=True, cmap="RdBu_r", method = "complete", metric = "euclidean", figsize=(20, 20), col_colors=sns.color_palette(cell_tf_values_colors))
-plt.setp(hmap.ax_heatmap.yaxis.get_majorticklabels(), rotation=0)
-plt.savefig('final_full_cluster_heatmap_gro_rna.png')
 
-labels = [item.get_text() for item in hmap.ax_heatmap.yaxis.get_majorticklabels()]
-labels.reverse()
-with  open("final_full_cluster_heatmap_gro_rna.csv", 'wb') as csv_file:
-    wr = csv.writer(csv_file,dialect='excel',quoting=csv.QUOTE_ALL)
-    for tf in labels:
-        wr.writerow([tf,])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D2'],cluster3_enhancers_expressed['ES_D5'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D2'],cluster3_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D2'],cluster3_enhancers_expressed['ES_D10'])
 
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D5'],cluster3_enhancers_expressed['ES_D7'])
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D5'],cluster3_enhancers_expressed['ES_D10'])
 
-# 4. Reorder based on clustering
-reorder_clustering = cell_tf_values_std.columns.values[hmap.dendrogram_col.reordered_ind]
-cell_tf_values_std_ordered = cell_tf_values_std[reorder_clustering]
-reindex_cluserting = cell_tf_values_std.index.values[hmap.dendrogram_row.reordered_ind]
-cell_tf_values_std_ordered = cell_tf_values_std_ordered.reindex(reindex_cluserting)
-cell_tf_values_std_ordered.to_csv("final_full_cluster_z_score_gro_rna.csv", encoding='utf-8')
+scipy.stats.ranksums(cluster3_enhancers_expressed['ES_D7'],cluster3_enhancers_expressed['ES_D10'])